Create app.py

app.py

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+import keras
+from keras.models import Model
+from tensorflow.keras.applications import densenet
+from keras import backend as K
+import tensorflow as tf
+from keras.layers import Dense, GlobalAveragePooling2D
+from keras.models import Model
+import numpy as np
+
+chexnet_weights_path = "brucechou1983_CheXNet_Keras_0.3.0_weights.h5"
+IMG_SIZE = 320
+
+base = densenet.DenseNet121(weights=None,
+                            include_top=False,
+                            input_shape=(IMG_SIZE,IMG_SIZE,3)
+                           )
+## workaround - add dummy layer then load weights then pop dummy layer, in order to match expected shape for pretrained weights
+predictions = tf.keras.layers.Dense(14, activation='sigmoid', name='predictions')(base.output)
+## ,by_name=True - could save on workaround, but don't know if names will necessarily match + how to validate? - https://github.com/keras-team/keras/issues/5397
+base = tf.keras.Model(inputs=base.input, outputs=predictions) 
+base.load_weights(chexnet_weights_path)
+print("CheXNet loaded")
+base.trainable=False # freeze most layers
+base.training=False
+
+base.layers.pop()
+
+base.layers.pop()
+
+### https://stackoverflow.com/questions/41668813/how-to-add-and-remove-new-layers-in-keras-after-loading-weights
+new_model = GlobalAveragePooling2D()(base.layers[-4].output) 
+new_model = Dense(4, activation='softmax')(new_model)
+chexnet_model = keras.Model(base.input, new_model)
+
+### Loading Weights from the saved Weights
+
+from keras.models import load_model
+chexnet_model.load_weights('./chexnet_with_data_aug_and standardized.05.hdf5')
+
+### User-Defined Functions for Pre-Processing
+
+labels = ['Negative for Pneumonia',
+ 'Typical Appearance',
+ 'Indeterminate Appearance',
+ 'Atypical Appearance']
+
+def get_mean_std(inp):
+    sample_data = []
+    sample_data.append(np.array(inp))
+    mean = np.mean(sample_data)
+    std = np.std(sample_data)
+    return mean, std
+
+def standardize_image(inp, preprocess=True):
+    """Load and preprocess image."""
+    mean, std = get_mean_std(inp)
+    if preprocess:
+        inp = np.array(inp, dtype=np.float32)
+        inp -= mean
+        inp /= std
+        inp = np.expand_dims(inp, axis=0)
+    return inp
+
+def preprocess_input(inp):
+    inp = standardize_image(inp, preprocess=True)
+    return inp
+    
+def get_prediction(inp):
+    prediction = chexnet_model.predict(inp)
+    confidences = {labels[i]: float(prediction[i]) for i in range(4)}
+    return confidences
+
+def classify_image(inp):
+    inp = standardize_image(inp, preprocess=True)
+    prediction = chexnet_model.predict(inp)
+    confidences = {labels[i]: float(prediction[0][i]) for i in range(4)}
+    return confidences
+
+### Application Code
+
+import gradio as gr
+
+title = "Decision Support System to Diagnose COVID-19 using Chest X-Ray"
+description = """
+A prototype app for classifying the four COVID-19 Pneumonia Classes, created as a Demo for Gradio and HuggingFace Spaces
+. It is currently able to classify a given image as one of the following four classes: Typical Appearance, Atypical Appearance, 
+Indeterminate Appearance and Negative for Pneumonia. The System has an interpretability component as well.
+"""
+
+examples = ['0a3afeef9c01.png']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(fn=classify_image,inputs=gr.inputs.Image(shape=(320, 320)),outputs=gr.outputs.Label(num_top_classes=4),
+title=title,description=description, examples=examples,
+             interpretation=interpretation,enable_queue=enable_queue).launch()